Our image contains objects at a wide range of distances, and the size of an object can vary from several thousand points to a single point.
We leverage the deep layer aggregation network architecture [1]} to effectively extract and combine multi-scale features.
Our network is fully-convolutional and consists of three hierarchical levels as shown in Figure REF .
The size of convolutional kernels at each level is 64, 64, 128 respectively.
